A Semantic Oriented Approach to Textual Entailment Using WordNet-Based Measures

Castillo, Julio Javier

doi:10.1007/978-3-642-16761-4_5

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Finally, our Textual Entailment engine utilizes eight WordNet-based similarity measures, such as proposed by the authors in [10], with the purpose of obtaining the maximum similarity between two concepts. These text-to-text similarity measures are based on the followings word-to-word similarity metrics: Resnik [11], Lin [12], Jiang & Conrath [13], Pirrò & Seco [14], Wu & Palmer [15], Path Metric, Leacock & Chodorow [16], and a semantic similarity to sentence level named SemSim [10].…”

Section: System Architecturementioning

confidence: 99%

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An Approach to Cross-Lingual Textual Entailment using Online Machine Translation Systems

Castillo¹,

Cardenas²

2011

Polibits

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In this paper, we show an approach to cross-lingual textual entailment (CLTE) by using machine translation systems such as Bing Translator and Google Translate. We experiment with a wide variety of data sets to the task of textual Entailment (TE) and evaluate the contribution of an algorithm that expands a monolingual TE corpus that seems promising for the task of CLTE. We built a CLTE corpus and we report a procedure that can be used to create a CLTE corpus in any pair of languages. We also report the results obtained in our experiments with the three-way classification task for CLTE and we show that this result outperform the average score of RTE (Recognizing Textual Entailment) systems. Finally, we find that using WordNet as the only source of lexical-semantic knowledge it is possibly to build a system for CLTE, which achieves comparable results with the average score of RTE systems for both two-way and three-way tasks.

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Section: System Architecturementioning

confidence: 99%

“…Finally, this procedure produces eight WordNet-based semantic similarity measures, which have been tested over monolingual textual entailment [10] achieving results that outperformed the average accuracy of the RTE systems.…”

Section: A Featuresmentioning

confidence: 99%

An Approach to Cross-Lingual Textual Entailment using Online Machine Translation Systems

Castillo¹,

Cardenas²

2011

Polibits

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“…In this paper, which is a expanded version of the paper [2], we address the RTE problem by using a machine learning approach. All feature sets are WordNet-based, aimed at measuring the benefit of WordNet as a knowledge resource to the RTE task.…”

Section: Introductionmentioning

confidence: 99%

A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

Castillo

2011

Int. J. Mach. Learn. & Cyber.

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In this paper we explain how to build a recognizing textual entailment (RTE) system which only uses semantic similarity measures based on WordNet. We show how the widely used WordNet-based semantic measures can be generalized to build sentence level semantic metrics in order to be used in both mono-lingual and cross-lingual textual entailment. We experiment with a wide variety of RTE datasets and evaluate the contribution of an algorithm which expands the RTE monolingual corpus. Results achieved with this method yielded significant statistical differences when predicting RTE test sets. We provide an efficiency analysis of these metrics drawing some conclusions about their practical utility in recognizing textual entailment. We also analyze the cross-lingual textual entailment task, we create a bilingual English-Spanish corpus, and propose a procedure to create a cross-lingual textual entailment corpus for any pair of languages. Finally, we show that the proposed method is enough to build an average score RTE system in both monolingual and cross-lingual textual entailment, that uses semantic information from WordNet as the only source of lexicalsemantic knowledge.

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A Semantic Oriented Approach to Textual Entailment Using WordNet-Based Measures

Cited by 2 publications

References 8 publications

An Approach to Cross-Lingual Textual Entailment using Online Machine Translation Systems

An Approach to Cross-Lingual Textual Entailment using Online Machine Translation Systems

A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

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